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XC 8967 



Bureau of Mines Information Circular/1984 




Analysis of Data on Respirable Quartz 
Dust Samples Collected in Metal 
and Nonmetal Mines and Mills 



By W. F. Watts, Jr., D. R. Parker, R. L. Johnson, 
and K. L. Jensen 



UNITED STATES DEPARTMENT OF THE INTERIOR 



Information Circular 8967 



Analysis of Data on Respirable Quartz 
Dust Samples Collected in Metal 
and Nonmetal Mines and Mills 



By W. F. Watts, Jr., D. R. Parker, R. L. Johnson, 
and K. L. Jensen 




UNITED STATES DEPARTMENT OF THE INTERIOR 
William P. Clark, Secretary 

BUREAU OF MINES 
Robert C. Horton, Director 




IA A rfl 



,\0 % 



Library of Congress Cataloging in Publication Data: 



Analysis of data on 


respirable quartz 


dust 


samples 


collected in 


metal 


and nonmetal mines and mills. 










(Bureau of Mines 


information circu 


ar ; 8967) 






Bibliography: p. 


21-22. 










Supt. of Docs, no 


.: I 28.27:8967. 










1. Mine dusts. 


2. Quartz dust. 


I. 


Watts, W 


F. (Winthrc 


P F.). 


II. United States. 


Bureau of Mines. 


HI. 


Series: 


Information 


circu- 


lar (United States. Bureau of Mines) ; 


8967. 








J TNB9WJ4 [TN312] 622s [622'. 8] 


83-600327 





CONTENTS 

Page 

Abstract. 1 

Introduction 2 

MSHA coding system 2 

Mine Inspection Data Analysis System (MIDAS) 3 

File structure 3 

Data editing 3 

Sampling strategy 4 

Results 6 

Data distribution 6 

Variables analysis 9 

Year and mine type. 9 

Commodity and occupation 9 

Commodity, location, and time.. 14 

Commodity and occupation group 16 

Summary and conclusions 20 

References 21 

Appendix A. — MSHA sampling codes and descriptors and selected GM C/TLV data.... 23 

Appendix B. — MSHA mine health ranking criteria 27 

Appendix C . — Abbreviations used in this report 28 

ILLUSTRATIONS 

1. Log-normal probability plot of C/TLV's for RQ 8 

2. Cumulative frequency plot of quartz concentrations grouped by location... 15 

3. Cumulative frequency plot of quartz concentrations grouped by commodity.. 18 

TABLES 

1. Contaminants most frequently sampled in metal and nonmetal mines 6 

2 . Number of mines and employment by mine type 7 

3. Yearly statistics for RQ, respirable and total nuisance dust, and total 

silica dust 10 

4. RQ exposures by mine type for 1974-81 and 1978-81 11 

5. Ranking of 24 commodities by 1978-81 RQ GM C/TLV 12 

6. Ranking of 28 occupations by 1978-81 RQ GM C/TLV 13 

7. RQ GM C/TLV by commodity, location, and time period 14 

8. Occupation groups with highest RQ exposures 17 

9. Other occupation groups with high RQ exposures 19 

A-l. MSHA contaminant codes for personal samples 23 

A-2 . MSHA contaminant codes for area samples 24 

A-3. RQ samples collected 1974-81 for each MSHA occupation code.. 24 

A-4. RQ samples collected 1974-81 for each commodity 25 

A-5. RQ samples from metal and nonmetal mines, by location 25 

A-6. GM C/TLV's for 9 occupation groups 26 

A-7. GM C/TLV's for 8 commodity groups 26 





UNIT OF MEASURE ABBREVIATIONS USED IN 


THIS REPORT 


dBa 


decibel (A scale) ppm 


parts per million 


cm 3 


cubic centimeter yg/m 3 


microgram per cubic 
meter 


h 


hour 






Mm 


micrometer 


mg/m 3 


milligram per cubic meter 






WL 


working level 


mppcf 


million particles per 






cubic foot yr 


year 


pet 


percent 





ANALYSIS OF DATA ON RESPIRABLE QUARTZ DUST SAMPLES COLLECTED 
IN METAL AND NONMETAL MINES AND MILLS 

By W. F. Watts, Jr., ' D. R. Parker, 2 R. L. Johnson, 3 and K. L. Jensen 4 



ABSTRACT 

This report describes a statistical analysis of 41,502 respirable 
quartz dust samples collected from 1974 through 1981 in metal and non- 
metal mines and mills. The goal of this Bureau of Mines study was to 
identify commodities and occupations associated with high quartz dust 
exposure so that future enforcement and research activity can be focused 
on the high-exposure areas. 

For this analysis, the Bureau used its computerized Mine Inspection 
Data Analysis System (MIDAS), which contains data on nearly 350,000 air 
samples, including data for 61 contaminants in 45 commodities. The sam- 
ples analyzed were collected by inspectors of the Mine Safety and Health 
Administration (MSHA). The data were entered into the MIDAS computer 
and coded according to commodity, occupation, location, and other cate- 
gorical information. Occupations and commodities with high quartz dust 
exposure were identified by grouping the data by code categories. 

Workers at sandstone, clay and shale, and miscellaneous nonmetallic 
mineral mills had the highest quartz dust exposures. Within these 
mills, occupations with high exposures were baggers, general laborers, 
and persons involved in crushing, grinding, and sizing operations. Dust 
concentrations in these mills were not necessarily the highest, but be- 
cause the dust contained a high percentage of quartz, the health risk 
from dust exposure was the greatest. 

1 1ndustrial hygienist, Twin Cities Research Center, Bureau of Mines, Twin Cities, 
MN. 

^Program analyst, Mine Safety and Health Administration, U.S. Department of Labor, 
Arlington, VA. 

-'Operations research analyst, Division of Automatic Data Processing, Bureau of 
Mines, Denver, CO. 

^Math aide, Twin Cities Research Center (now graduate student, Iowa State Univer- 
sity, Ames, IA) . 



INTRODUCTION 



The 1980 National Academy of Science 
report on respirable dust in mines (_O s 
concluded that there was a critical need 
to assess the magnitude of respirable 
dust exposures in noncoal mines. Expo- 
sure to respirable quartz dust (RQ) 6 has 
been linked to silicosis, a form of pul- 
monary fibrosis. Pulmonary fibrosis is 
disabling, progressive, and sometimes 
fatal. Silicosis tends to occur after 
years of exposure, but occasionally high 
exposures of only a year or more lead to 
acute silicosis (2). To assist research- 
ers and enforcement personnel in their 
efforts to protect workers from RQ haz- 
ards, the Bureau of Mines undertook a 
statistical analysis designed to identify 
mine and mill commodities and occupations 
associated with high quartz dust expo- 
sure. The results of this analysis are 
presented in this report. 

The Federal Government agency respon- 
sible for establishing and enforcing mine 
health and safety standards is MSHA. 



MSHA inspectors determine compliance with 
metal and nonmetal mine and mill Federal 
health standards through the collection 
and analysis of environmental air sam- 
ples. They collect many types of samples 
for a variety of contaminants in noncoal 
mines and mills , and these samples con- 
stitute a large body of historical indus- 
trial hygiene data. Since 1974 MSHA has 
collected nearly 350,000 samples in metal 
and nonmetal mines and mills, and records 
of these exposures are stored in MIDAS, a 
computerized information system operated 
by the Bureau (3). MIDAS is a data base 
and software system designed to analyze 
the industrial hygiene data collected by 
MSHA, and to associate these data with 
other mine information collected from 
other sources. MIDAS is accessible from 
remote terminals around the country via 
the Bureau's telecommunications network. 
Certain aspects of the system are dis- 
cussed in this report, and a description 
of the software used in MIDAS has been 
previously reported (4). 



MSHA CODING SYSTEM 



MSHA uses a coding system to describe 
the occupational environments where per- 
sonal samples (obtained from monitors 
worn by personnel) are collected. 

The codes are substituted for a compre- 
hensive written description of the work- 
place in order to facilitate automated 
data processing. They provide a descrip- 
tion of the industry (commodity produced 
at the mine site), operation (occupation 
of the person monitored for sampling), 
and a location (mine type) for personal 
sample collected. Personal samples are 
collected over an entire workshift, and 

^Underlined numbers in parentheses re- 
fer to items in the list of references 
preceding the appendixes. 

"Except for unit of measure abbrevia- 
tions, all abbreviations are identified 
the first time they are used and are also 
listed in appendix C. (Unit of measure 
abbreviations are listed after the table 
of contents. ) 



for each sample an individual computer 
record is maintained. Other valuable in- 
formation in the computer record includes 
mine identification number, date, contam- 
inant measured, concentration, threshold 
limit value (TLV), and social security 
number of the miner. A typical set of 
codes might describe a respirable dust 
sample collected for a load-haul -dump 
diesel operator in a limestone quarry in 
Illinois. 

The approach used in this analysis was 
to calculate exposures for different pop- 
ulations of workers. The codes were used 
individually or in combinations to define 
population groups. Grouping similar 
codes together helped compensate for the 
recurring problem of inadequate sample 
size. A complete discussion of the 
groups used in the analysis is included 
in the "Results" section. Complete lists 
of the descriptors used for contaminant, 
commodity, location, and occupation are 
in appendix tables A-l through A-5. 



MINE INSPECTION DATA ANALYSIS SYSTEM (MIDAS) 



FILE STRUCTURE 

MIDAS includes 5 master files that con- 
tain over 400,000 records. The master 
files are used to create smaller files 
for analysis. The master files are known 
as the personal exposure file, the area 
sample file, the Minerals Availability 
System (MAS) data base, the 22-mine sur- 
vey file, and the mines file; and each 
has a different format. The RQ data are 
contained in the edited personal exposure 
file, which is described in the next 
section. 

The mines file is an index of all 
metal and nonmetal mine properties in 
the United States. Each property is 
listed with a unique mine identification 
number, its location, the property name, 
company name, approximate number of 
employees (if any), year-round or other 
status, and other data. Information from 
this file is stripped off and added 
to records of personal and area sam- 
ples, thus allowing records from similar 
commodities and mine types to be sorted 
and grouped together. This sorting and 
grouping is possible because every 
record in MIDAS, regardless of its 
format, has a mine identification number 
of seven digits. Thus all data are 
cross-referenced by mine identification 
numbers. 

Worldwide commodity information for 
about 200,000 mine properties is avail- 
able in the MAS data base. Data from 
this file contribute to the ability of 
MIDAS to group producers of similar com- 
modities together for analysis, particu- 
larly when mine properties that produce 
both primary and secondary products are 
involved. Often the first clue point- 
ing to a potentially hazardous exposure 
comes from knowledge of what product or 
products are mined. The classification 
system used in the MAS is more 
extensive than that available in the 
mines file. Among the sources of data 
gathered for the MAS are mine oper- 
ators, government agencies, and published 
literature. 



In 1976-77 MSHA conducted an environ- 
mental health survey of 22 underground 
metal and nonmetal mines with assistance 
from the National Institute of Occupa- 
tional Safety and Health (NIOSH) and the 
Bureau. The results of this survey are 
in the 22-mine survey file. (The Public 
Health Service and the Bureau had previ- 
ously surveyed some of the same mines in 
the late 1950' s.) About 17,000 personal 
and area samples have been collected at 
the 22 mines. These samples are the 
only industrial hygiene data in MIDAS 
that were not collected during mine 
inspections. 

The area record file contains 136,174 
records of area samples. An area sample 
is a grab sample collected by a sampling 
device (detector tube, bistable, vacuum 
bottle, instantaneous monitor, glass 
impinger, or charcoal tube) located near 
the worker. The area samples are for 
toxic and asphyxiant gases, mists and 
vapors, and radon daughters. 

The RQ data are contained in the per- 
sonal exposure file. This file also con- 
tains data on occupational exposures to 
other airborne contaminants regulated by 
MSHA. The data were collected by moni- 
toring miners over the course of an 
entire workshift. The file, as received 
from MSHA, contained 206,888 records of 
personal exposure, of which 183,995 (89 
pet) passed an editing process. Editing 
was necessary because of errors in the 
records. These errors included duplicate 
entries, decimal place errors, coding 
errors, erroneous TLV's, and misclassifi- 
cation of samples. 

DATA EDITING 

MSHA regulates health and safety condi- 
tions in mines under the authority of the 
Federal Mine Safety and Health Act of 
1977 (5). The specific regulations are 
found in the Code of Federal Regulations, 
Title 30, (£), but for RQ and other air- 
borne contaminants, MSHA adopted the 
1973 recommended TLV's of the American 
Conference of Governmental Industrial 



Hygienists (ACGIH) (7). The TLV for RQ 
is determined by collecting a respirable 
dust sample, analyzing for quartz con- 
tent, 7 and calculating the TLV using the 
formula 

10 mg/m 3 

percent respirable quartz + 2 

when the quartz content (percent respir- 
able quartz) is XL pet. (The resultant 
TLV is expressed in milligrams per cubic 
meter. ) The TLV for respirable dust con- 
taining quartz is therefore inversely 
proportional to the quartz content of the 
sample. Thus, for a given exposure lev- 
el, the magnitude of toxicity is propor- 
tional to the quartz content (8). 

Respirable dust is capable of deep lung 
penetration and is generally defined as 
that fraction of an aerosol which in- 
cludes particles with an aerodynamic 
diameter of less than 5 um. However, the 
size classifiers used with personal sam- 
plers do not have a sharp size cutoff; 
they reject some dust particles with di- 
ameters less than 5 pm and allow small 
numbers of larger particles to pass (8). 
The factor 2 in the denominator of the 
TLV formula ensures that dust exposures 
will not be excessively high when the 
quartz content is less than 5 pet and 
effectively limits the dust concentration 
to 5 mg/m 3 when no quartz is identified 
in the sample. 

The MIDAS records of RQ exposure were 
edited using the TLV formula. By substi- 
tuting 1.0 and 100 for percent respirable 
quartz in the formula, it was determined 



that the TLV for RQ-bearing dusts must be 
between 0.10 and 3.33 mg/m 3 . Therefore, 
when the TLV recorded in a record fell 
within this range, the record was con- 
sidered valid; otherwise, the data were 
recoded based upon alternative rules (3). 

Editing the RQ records resulted in re- 
tention of 75 pet of the original rec- 
ords, the creation of 11,582 respirable 
nuisance dust records (21 pet of the 
original records), the recoding of 937 
records (1.7 pet), and the rejection of 
1,047 records (1.9 pet). The number of 
respirable nuisance dust records created 
during the editing process was large be- 
cause MSHA inspectors have only one code 
for respirable dust regardless of quartz 
content. MIDAS was used to create a code 
for respirable nuisance dust when the TLV 
exceeded 3.33 mg/m 3 , which indicated the 
sample contained less than 1.0 pet 
quartz. 

Other important features of the editing 
process were additions made to each rec- 
ord to simplify sorting and analysis. 
MIDAS calculated the concentration-to-TLV 
ratio (C/TLV) for every record of per- 
sonal exposure and added it to each rec- 
ord. The percentage of silica in every 
dust sample was also calculated and added 
to the records. The following informa- 
tion from the mines file was added to 
each record: commodity code, mine type, 
mine status, size group, and number of 
employees at the sampled mine. For com- 
puting geometric statistics (statistics 
based on a log transformation) , concen- 
trations reported as zero were considered 
equal to 0.001 mg/m 3 . 



SAMPLING STRATEGY 



MSHA inspectors collect samples to en- 
sure compliance with the 1973 ACGIH 
TLV's, which have been adopted as stan- 
dards by MSHA. The samples are not 
collected as they would be in a scientif- 
ically designed survey; rather, sampling 

'Quartz content is determined by X-ray 
diffraction after the filter has been 
weighed. 



is judgmental. This creates a statisti- 
cal problem: These data may not be 
representative, so the classical sta- 
tistical assumptions of randomness, homo- 
scedasticity (homogeneous variance), and 
normal distribution may not apply. The 
factors discussed below (in order of rel- 
ative importance) are believed to influ- 
ence MSHA's sampling strategy. 



The 1977 Mine Safety and Health Act 
mandates that each underground mine be 
inspected four times per year and each 
surface mine or mill must be inspected 
two times per year. This requirement 
reflects the general opinion of Congress 
and the miners * unions that a Federal 
presence is important at every mine, and 
that underground mines are more hazardous 
than surface mines and mills. 

MSHA establishes mine health ranking 
criteria which are used as sampling 
guidelines (9). (The criteria are listed 
in part in appendix B.) In fiscal year 
1982, these guidelines included three 
ranks: A, B, and C. Rank A lists mine 
categories known to have the greatest 
health risks. Mines with a history of 
noncompliance receive more frequent 
inspections. However, resampling of an 
area previously out of compliance is not 
done unless the mine operator has insti- 
tuted environmental controls or a per- 
sonal protection program. The mine oper- 
ator may request additional sampling to 
demonstrate the effectiveness of the con- 
trol measures, which may result in a 
cluster of samples being collected at 
certain properties. Inspectors are in- 
structed to sample employees having the 
greatest potential for exposure, and this 
determination depends upon the judgment 
of the inspector. In addition, the num- 
ber of employees at the mine site affects 
the proportion of employees sampled and 
to some extent the number of inspectors 
required per visit. An illustration of 
the effect commodity and employment have 
on sample distribution was provided in a 
previous report (3) where it was shown, 
for instance, that 0.13 samples were col- 
lected per employee from molybdenum prop- 
erties and 0.41 samples were collected 
per employee from limestone properties. 
From the same properties, 7 samples were 
collected per limestone property in con- 
trast to more than 50 samples per molyb- 
denum property. In this case there were 
many small limestone mines with few 
employees, but only a few large molyb- 
denum mines with many employees . 

Market forces affect the number of 
mines of any one type which are in 



operation, and their production and em- 
ployment levels. Mines inspected one 
year may not be inspected the next, be- 
cause of strikes, mine shutdowns or clo- 
sures which result from a drop in com- 
modity price or declining ore quality, or 
other similar circumstances. 

MSHA is affected by changes in budget 
appropriations and administrative poli- 
cies. These changes affect the number 
and location of MSHA district offices, 
the number of inspectors, and the number 
and types of inspections conducted each 
year. 

Since much of MSHA's sampling strategy 
is "worst case," it is nonrandom and may 
be biased. However, the degree and 
direction of all possible biases are ob- 
scure. Several factors suggest that 
MSHA's data are representative. Compan- 
ies are not given prior notice of inspec- 
tions, so operators cannot make special 
preparations. No operator can deny an 
MSHA inspector access to its property, 
thus every mine can be inspected. This 
differs from a research survey in which 
mines are asked to participate and know 
well in advance when samples will be col- 
lected. Although inspectors are in- 
structed to select workers with the 
greatest potential for overexposure, most 
of the samples reported had low dust con- 
centrations. In attempting to pick out 
"high-risk" workers, the inspectors can- 
not know if individual workers will ac- 
tually encounter high dust exposures. 
However, the results were typical of 
environmental sampling. Environmental 
samples are typically log-normally dis- 
tributed, meaning that many low concen- 
trations are reported. The authors 
therefore believe the MSHA data are typi- 
cal of the results that would be expected 
from a survey using sampling strategies 
designed to assure randomness. The re- 
sults suggest that the MSHA inspectors, 
using common sense and judgment, col- 
lected "representative" samples within 
each occupation, location, and commodity 
group. Finally, certain commodities, oc- 
cupations, and locations are sampled so 
frequently that MSHA may approach a cen- 
sus of those mining subpopulations. 



RESULTS 



DATA DISTRIBUTION 

The first part of the analysis was 
aimed at determining (1) the distribution 
of the number of samples by contaminant, 
commodity, location, and occupation and 
(2) the best measures of exposure. Table 
1 shows that 12 contaminants accounted 
for 94 pet of all the samples collected, 
and that RQ samples accounted for 13 pet, 
ranking it second to noise (28 pet). 
However, if the samples for RQ, total 
nuisance dust, 8 respirable nuisance 

8 Total nuisance dust has a TLV of 1 
mg/m-*. This value is. used when samples 
collected for total dust contain less 
than 1 .0 pet quartz. Total dust samples 
are collected on filters without a cy- 
clone preclassifier to remove nonrespir- 
able particulate. 

^Respirable nuisance dust has a TLV of 
5.0 mg/m-*. This value is used when sam- 
ples collected for respirable dust 
contain less than 1.0 pet quartz. Res- 
pirable dust samples are collected on 
filters with a cyclone preseparator ahead 
of the filter to remove nonrespirable- 
size particulate. 



dust, 9 and total silica dust 10 are com- 
bined, they acount for about 25 pet of 
all the industrial hygiene samples col- 
lected. Tables A-l and A-2 in appendix A 
list all airborne contaminants for which 
personal or area samples were collected, 
and tables A-3 through A-5 show the num- 
ber of RQ samples distributed by com- 
modity, location, and occupation. It can 
be seen from these tables that the number 
of RQ samples collected varied consider- 
ably from code to code. 

From 1974 through 1981 MSHA collected 
41,502 RQ samples at 4,815 mine and mill 
properties on 32,188 miners. From 
table 2 it can be calculated that these 
samples were collected at 30 pet of the 
properties on 12 pet of the workforce. 

10 Total silica dust has a TLV deter- 
mined by the formula 

30 mg/m-* 



percent quartz + 3 



This value is used when a dust sample 
contains 1.0 pet or more quartz. 



TABLE 1. - Contaminants most frequently sampled in metal and nonmetal mines 



Contaminant 



Type of 
sample 



Number of 
samples 



Percentage of 
total samples 



Noise 1 

RQ 

Methane 

Carbon monoxide 

Radon daughter 

Carbon dioxide 

Oxygen 

Total nuisance dust 

Total silica dust 

Respirable nuisance dust 2 

Nitrogen dioxide 

Hydrogen sulfide 



Subtotal, 

Others , 

Total..., 



P 
P 
A 
A 
A 
A 
A 
P 
P 
P 
A 
A 



89,830 
41,566 
28,217 
28,170 
27,311 
24,292 
15,663 
13,415 
12,515 
11,582 
9,261 
2,450 



304,272 
17,897 



322,169 



27.9 
12.9 
8.7 
8.7 
8.5 
7.5 
4.9 
4.2 
3.9 
3.6 
2.9 
.8 



94.4 
5.5 



100.0 



A Area. P Personal. RQ Respirable quartz dust. 

includes both noise dosimeter and sound level meter measurements. 

2 Category created during editing process. 



TABLE 2. - Number of mines and employment by mine type 



Mine type 



Underground j Surface Mill 



Total 





NUMBER OF MINES 








829 

213 



133 


507 
1,069 
6,554 
3,759 


585 

757 



1,863 


1,921 




2,039 




6,554 




5,755 




1,175 


11,889 


3,205 


16,269 



EMPLOYMENT 



Metal , 

Nonmetal. , 

Sand and gravel. 

Stone 

Total , 



28,547 

9,987 



2,081 



39,915 



30,152 
12,184 
35,725 
34,557 



112,618 



37,397 

25,157 



43,792 



106,346 



96,096 
46,628 
35,725 
80,430 



258,879 



NOTE. — Based on final 1981 figures from MSHA, Health and Safety 
Analysis Center, which show 1,182 uniquely identified mills and 
2,023 associated mills. 



These are crude estimates because the 
number of properties and employment fluc- 
tuate from year to year, but it is clear 
that a large percentage of the mines, 
mills, and workforce was sampled. Over- 
all, the average number of samples per 
mine sampled was 8.6, and the average 
number of samples per employee sampled 
was 1.3. 

Table 2 provides information on employ- 
ment and mine type which is essential for 
understanding the numerical distribution 
of the RQ samples. A majority of the 
mines were either sand and gravel pits or 
stone quarries with relatively few em- 
ployees, while the largest segment of the 
workforce was employed at very large 
metal mines and mills. The average num- 
ber of employees at an underground metal 
mine is 50, whereas the average number of 
people employed at a sand and gravel pit 
is 5. Nearly every worker can be sampled 
at a sand and gravel pit during the 
course of an inspection; but because of 
the large number of pits, samples are not 
collected at every site visit. In con- 
trast, the relatively few large under- 
ground metal mines are visited fre- 
quently, but only a tiny percentage of 
the workforce is sampled each time. 
Thus, the number of RQ samples collected 
was affected by the number of inspections 
required by the 1977 Mine Health and 
Safety Act, the number and types of mines 



and mine employment, and the degree 
of hazard perceived by individual 
inspectors. 

In an array of data such as the RQ 
data, frequency distributions and proba- 
bility ploys are useful in selecting the 
appropriate geometric or arithmetic sta- 
tistics to estimate central tendency and 
dispersion. A previous report (3) used 
these techniques to show that the RQ con- 
centrations and C/TLV's reported from 
1974 through 1980 approximated a log- 
normal distribution. This finding sug- 
gested that the geometric mean (GM) and 
geometric standard deviation (GSD) were 
better indicators of central tendency and 
dispersion than the corresponding arith- 
metic values. Figure 1 is a log-normal 
probability plot of the RQ C/TLV's from 
1974 through 1981. The figure suggests 
that the distribution of C/TLV's approxi- 
mates a log-normal distribution, although 
extreme values are present at both tails. 
(A description of the computer program 
used to generate this plot can be found 
in reference 10.) 

A straight-line normal probability plot 
indicates that a population is normally 
distributed. The abscissa of a point is 
an observed value, and the ordinate is 
its expected standard normal value, which 
is computed by ordering the observed val- 
ues, assigning a cumulative probability 



_l 
< 
> 



O 



O 
LU 
h- 
O 
LU 
Q_ 
X 
LU 



4.5 



3.6 



2.7 



.8 







-.9 



-1.8 



-2.7 



-3.6 



4.5 
-2. 



■A 
A 

A 
A 
A 
rA 
A 
A 
A 







A 
A 



A AA 



AAAA A 
AAAA 
AAAA 
AAAA 

AAAA 
AAA 
AAA 
AAAA 
AA 
AAA 
AAA 
AAA 
AAA 
AA 
AAA 
AA 
AA 

AAA 

AA 
AA 
AAA 
AA 
AA 
AA 
AA 
AA 
AA 
AA 
AA 
A A 
A 
AA 
A A 
A A 
A 



I 



-1.40 -700 



.700 1.40 

LOG C/TLV 



2.10 



2.80 



3.50 



FIGURE 1. - Log-normal probability plot of C/TLV's for RQ. 



value to each based upon its rank order 
within the sample, and then determining 
the standard normal value corresponding 
to that cumulative probability value. A 
standard normal value is a value on the 
normal distribution (or log-normal dis- 
tribution in this case) with a mean equal 
to zero and a standard deviation equal 
to one. The absolute value of the ex- 
pected standard normal value of a par- 
ticular observed value can be understood 
as the number of standard deviations 



away from the mean that an observed value 
of that rank would be expected to lie. 
Thus, an observed value is related to a 
cumulative probability value, which is 
then related to an expected standard nor- 
mal value. If the plot representing 
these relationships is close to a 
straight line, the population may be nor- 
mally distributed (11). 

For log-normally distributed data, the 
GM C/TLV is an excellent index of 



exposure because it relates the concen- 
tration to the TLV, which is determined 
by the quartz content of the sample. 
This allows an immediate assessment of 
exposure. A C/TLV greater than 1 indi- 
cates that an overexposure has occurred. 
Other measures of exposure are the geo- 
metric mean concentration (GM CONC), the 
median concentration, and the percentage 
of samples greater than the TLV. The 
concentration of quartz dust can also be 
calculated (percent quartz x concentra- 
tion) and compared to the NIOSH recom- 
mended permissible exposure limit (PEL) 
of 50 ug/m 3 crystalline silica ( 13 ) or 
the maximum allowable level of 100 ug/m 3 
under the current TLV (12). The GM 
C/TLV, the percentage of samples greater 
than the TLV, and quartz concentration 
are used in the next section to assess 
exposure. 

VARIABLES ANALYSIS 

Year and Mine Type 

The second part of the RQ analysis was 
based on partitioning of the RQ data by 
year, commodity, occupation, location, 
and combinations of variables. Such an 
analysis was possible because each record 
of exposure includes supplementary coded 
information. However, as shown in the 
previous section, the frequency with 
which each code was used varied consider- 
ably, and this was the limiting factor in 
grouping the data. 

Table 3 shows the yearly statistics for 
RQ; and for comparison, similar statis- 
tics are given for respirable nuisance, 
total nuisance, and total silica dust. 
It appears that the RQ dust levels were 
on a downward trend until 1980 and 1981, 
when slight increases occurred; but in 
those same years there was a sharp de- 
crease in the number of samples col- 
lected, which suggests a shift in dust 
sampling strategy. Each year, 15 to 20 
pet of the RQ dust samples equaled or 
exceeded the TLV, which demonstrates the 
continuing problem mines and mills have 
with RQ. 



The average TLV and the average quartz 
content can be calculated from the GM RQ 
CONC and the GM C/TLV shown in table 3. 
These two averages provide a crude esti- 
mate of the permissible level of dust in 
metal and nonmetal mine environments 
under existing standards. For all years 
the average TLV was 1.16 mg/m 3 and the 
average quartz content was 6.6 pet. In 
comparison, the GM TLV for total silica 
dust was 3.91 mg/m 3 and the average 
quartz content was 4.7 pet. 

Table 4 shows RQ exposure by mine type. 
The data were grouped according to the 
codes shown in tables A-4 and A-5. Table 
4 also shows a third variable, time; the 
RQ data for all years (1974-81) are shown 
on the left side of the table, and data 
for only the last 4 yr (1978-81) are 
shown on the right. Most of the samples 
were collected in surface stone mines, 
mills, and quarries, which would be ex- 
pected given the large number of these 
facilities. The highest concentrations 
of dust were found in samples from under- 
ground stone mines, but the highest GM 
C/TLV *s were found in samples from non- 
metal mills. 

Commodity and Occupation 

Many of the samples were collected at 
clay and shale, barite, and miscellaneous 
nonmetal mills, and a high proportion of 
the samples from these mills exceeded the 
TLV (table 5). In this analysis, the 
category "miscellaneous nonmetals" was 
used to clarify MSHA's commodity code 
number 59, which is termed "other non- 
metal. " This commodity group includes 
three or four producers of tripoli. 
Tripoli is typically ground to a fine 
particle size and contains a high per- 
centage of quartz; as a result, the TLV 
is low. Samples collected at these two 
mills severly skew the data for the en- 
tire commodity group. Higher concentra- 
tions of dust are tolerable at most other 
facilities because the dust contains a 
lower percentage of quartz, which in- 
creases the TLV. 



10 



TABLE 3. - Yearly statistics for RQ, respirable and total nuisance dust, and total 
silica dust 



Year 



Total 
samples 



> TLV, pet 



Concentration, mg/m c 



GM 



Median GSD 



GM C/TLV 



RQ 



1974, 
1975, 
1976. 
1977, 
1978. 
1979. 
1980. 
1981. 



Total or average, 



284 
2,652 
6,232 
7,579 
7,854 
7,806 
4,750 
4,345 



41,502 



46.13 
28.39 
25.69 
19.44 
16.16 
14.16 
15.24 
17.35 



18.82 







.58 
.49 
.51 
.44 
.40 
.38 
.40 
.43 



.43 



0.54 
.47 
.48 
.43 
.38 
.35 
.37 
.40 



.40 



2.62 
2.95 
3.12 
3.02 
3.04 
3.07 
2.71 
2.61 



2.98 



0.86 
.53 
.45 
.37 
.34 
.32 
.33 
.36 



.37 





RESPIRABLE NUISANCE DUST 








1974 


8 
479 
1,309 
1,840 
2,312 
1,803 
1,624 
2,149 




8.35 
3.82 
2.77 
3.16 
2.50 
.99 
1.26 


0.18 
.52 
.36 
.29 
.23 
.23 
.14 
.11 


0.12 
.56 
.39 
.45 
.36 
.27 
.13 
.12 


2.87 
5.70 
5.65 
8.12 
9.76 
7.43 
6.49 
8.06 


0.04 


1975 


.10 


1976 


.07 


1977 


.06 


1978 


.05 


1979 


.05 


1980 


.03 




.02 




11,524 


2.62 


.21 


.27 


7.99 


.04 





TOTAL 1 


NUISANCE DUST 










1974 


67 
436 
1,194 
1,600 
2,061 
3,213 
3,035 
1,761 


25.37 
40.37 
34.17 
34.00 
25.52 
20.67 
14.53 
16.47 


3.95 
6.14 
4.59 
4.65 
3.09 
2.22 
1.59 
1.86 


4.19 
7.47 
5.10 
5.62 
4.17 
3.00 
2.16 
2.70 


3.72 
6.36 
5.97 
6.08 
7.40 
7.69 
7.19 
8.24 


0.04 


1975 


.61 


1976 


.46 


1977 


.46 


1978 


.31 


1979 


.22 


1980 


.16 




.19 




13,367 


22.94 


2.55 


3.43 


7.49 


.26 





TOTAL 


SILICA DUST 










1974 


34 
196 
781 
1,622 
1,693 
3,823 
3,291 
1,071 


41.18 
66.84 
42.25 
39.27 
22.15 
19.88 
21.67 
23.06 


1.92 
6.50 
3.29 
2.78 
1.43 
1.41 
1.66 
1.62 


1.65 
8.28 
3.13 
2.71 
1.46 
1.30 
1.50 
1.51 


4.29 
4.20 
3.68 
4.80 
4.90 
3.69 
3.35 
4.29 


0.69 


1975 


1.59 


1976 


.76 


1977 


.69 


1978 


.35 


1979 


.37 


1980 


.43 




.42 




12,511 


25.63 


1.76 


1.61 


4.10 


.45 



GM 

GM C/TLV 

GSD 

RQ 

TLV 



Geometric mean. 

Geometric mean concentration-to-TLV ratio, 

Geometric standard deviation. 

Respirable quartz dust. 

Threshold limit value. 



11 



TABLE 4. - RQ exposures by mine type for 1974-1981 and 1978-1981 1 



Industry group 



1974-81 



1978-81 



UG 
mine 



Surface 
mine 



Mill 



Total 



UG 
mine 



Surface 
mine 



Mill Total 



Stone: 

N 

GM CONC mg/m 3 .. 

GM C/TLV 

Metal: 

N 

GM CONC mg/m 3 .. 

GM C/TLV 

Nonmetal: 

N 

GM CONC mg/m 3 .. 

GM C/TLV 

Sand and gravel: 

N 

GM CONC mg/m 3 .. 

GM C/TLV 

Total: 

N 

GM CONC mg/m 3 .. 

GM C/TLV 



797 
0.79 
0.40 

3,301 
0.56 
0.45 

631 
0.76 
0.57 


NAp 
NAp 

4,730 
0.62 
0.45 



12,745 
0.36 
0.28 

1,474 
0.27 
0.26 

1,471 
0.39 
0.34 

5,180 
0.27 
0.29 

20,869 
0.33 
0.29 



7,071 
0.55 
0.45 

2,942 
0.43 
0.40 

3,314 
0.77 
0.70 

1,545 
0.37 
0.59 

14,872 
0.54 
0.50 



20,613 
0.43 
0.34 

7,717 
0.44 
0.39 

5,416 
0.64 
0.56 

6,725 
0.29 
0.35 

40,471 
0.43 
0.37 



602 
0.73 
0.36 

1,516 
0.51 
0.40 

299 
0.67 
0.47 


NAp 
NAp 

2,418 
0.58 
0.40 



8,505 
0.36 
0.27 

1,007 
0.26 
0.24 

729 
0.34 
0.28 

3,701 
0.27 
0.29 

13,941 
0.32 
0.28 



3,525 
0.51 
0.45 

1,611 
0.39 
0.35 

1,718 
0.72 
0.64 

942 
0.39 
0.59 

7,796 

0.50 
0.46 



12,612 
0.41 
0.31 

4,134 
0.39 
0.34 

2,746 
0.56 
0.50 

4,643 
0.29 
0.34 

24,155 
0.40 
0.34 



GM CONC Geometric mean concentration. 

GM C/TLV Geometric mean concent rat ion-to-TLV ratio. 

N Sample size (number of samples). 

NAp Not applicable. 

UG Underground. 

Excluded are 1,031 samples which could not be class 
the mines from which they were taken are permanently 
leted from the master mine file. 



ified by industry 
closed and were 



group because 
therefore de- 



The 24 commodities listed in table 5 
account for 97 pet of all the samples. 
The commodities are ordered according to 
their 1978-81 GM C/TLV s, and the per- 
centage of samples that exceeded the TLV 
is shown. (Miscellaneous nonmetals en- 
compasses nonmetals not elsewhere classi- 
fied, such as aplite, vermiculite, ky- 
anite, and tripoli; and miscellaneous 
stone includes a variety of products such 
as basalt, diabase, gabbro, and others.) 
Each mine was assigned a commodity code, 
but some mines produce more than one 
product, so the classification system was 
not perfect. The periods 1974-77 and 



1978-81 were chosen because the 1977 Mine 
Health and Safety Act resulted in changes 
in the MSHA inspection program, which in 
turn affected the sampling strategy. 

With the exception of traprock, each GM 
C/TLV was lower in 1978-81 than it was in 
the preceding 4 yr, which suggests there 
was improvement in environmental condi- 
tions. The GM C/TLV ranking of the first 
seven commodities in table 5 listed would 
be the same for 1974-77 as for 1978-81, 
except that feldspar would be rated sec- 
ond rather than twentieth. 



12 



TABLE 5. - Ranking of 24 commodities 1 by 1978-81 RQ GM C/TLV 
(In order of decreasing GM C/TLV for 1978-81) 



Commodity 



1974-77 



N 



> TLV, pet 



GM C/TLV 



1978-81 



N 



> TLV, pet 



GM C/TLV 



Barite 

Misc. nonmetals 

Molybdenum 

Clay and shale 

Sands tone 

Misc. stone 

Gold and silver 

Granite 

Copper 

Talc 

Lead and zinc 

Slate 

Traprock 

Fluorspar 

Sand and gravel 

Lime 

Cement 

Phosphate. 

Uranium 

Feldspar 

Mica 

Iron 

Limestone 

Gypsum 

Total 

Total metal and nonmetal. 



84 

242 

348 

1,723 

1,363 

161 

259 

1,236 

1,214 

54 

566 

137 

307 

56 

2,082 

193 

1,264 

341 

487 

65 

18 

650 

3,308 

34 



16,212 
16,747 



61.9 
43.4 
34.2 
34.9 
37.4 
38.5 
28.2 
22.9 
21.7 
29.6 
18.2 
18.2 
11.1 
30.4 
21.8 
20.2 
18.3 
19.3 
18.5 
40.0 
16.7 
20.6 
14.4 
11.1 
ND 
23.2 



1.44 
.83 
.71 
.65 
.67 
.63 
.60 
.47 
.44 
.52 
.42 
.51 
.28 
.59 
.35 
.31 
.36 
.46 
.41 
.88 
.56 
.39 
.29 
.28 
ND 
.43 



56 
499 
178 

1,490 

1,767 
307 
615 

2,273 
785 
88 
339 
129 
404 
66 

4,643 
200 

1,083 
121 
610 
128 
116 

1,527 

6,444 
134 



24,002 
24,755 



26.8 

40.9 

30.9 

28.3 

29.9 

25.7 

16.1 

19.2 

18.2 

13.6 

11.8 

7.0 

17.3 

12.1 

15.1 

14.5 

10.8 

9.1 

6.2 

12.5 

6.0 

10.6 

6.6 

2.2 

ND 

15.1 



0.76 
.68 
.65 
.58 
.55 
.53 
.43 
.42 
.41 
.40 
.40 
.40 
.38 
.37 
.34 
.30 
.30 
.30 
.28 
.27 
.27 
.26 
.23 
.20 
ND 

.34 



GM C/TLV Geometric mean concent rat ion-to-TLV ratio. 

N Sample size (number of samples). 

ND Not determined. 

TLV Threshold limit value. 

1 Not included are marble, antimony, bauxite, beryl, chromite, manganese, 
tungsten, mercury, other metals, asbestos, boron, magnesite, potash, pumi 
sodium compounds, sulfur, gilsonite, and oil shale. 



titanium, 
ce, salt, 



Table 5 also shows the sample size (N) 
for each commodity group. For limestone 
the total N (1974-81) was large, 8,752 
samples; but for fluorspar total N was 
only 122 samples. The uneven distribu- 
tion of samples became a problem when 
further subgrouping by occupation, loca- 
tion, and time was attempted, because N 
sometimes became too small — or it became 
zero. This was especially true of the 
minor commodities. The problem was re- 
duced somewhat by grouping similar com- 
modities together when there was an 
insufficient number of samples. (This is 
discussed in more detail later. ) 



Table 6 lists 28 occupations which 
account for 97 percent of all the RQ sam- 
ples. The occupations are ranked accord- 
ing to their 1978-81 GM C/TLV's; and the 
sample size is included; and the per- 
centage of samples that exceeded the TLV 
is shown. The 1978-81 GM C/TLV for bag- 
gers, the first-ranked occupation, was 35 
pet higher than that of the second-ranked 
occupation, slushing; and nearly 40 pet 
of the bagger samples exceeded the TLV. 
A closer examination of the data for bag- 
gers showed that the product most often 
bagged is some type of industrial sand 
such as glass sand, frac sand, or silica 



13 



TABLE 6. - Ranking of 28 occupations 1 by 1978-81 RQ GM C/TLV 
(In order of decreasing GM C/TLV for 1978-81) 



Occupation 



1974-77 



N 



> TLV, pet 



GM C/TLV 



1978-81 



N 



> TLV, pet 



GM C/TLV 



Bagger 

Slushing 

Supply 

Blasting 

Grinding 

Welding 

Percussive drilling 

General labor. 

Drying, filtering, and 

thickening 

Crushing 

Rotary drilling 

Administration. 

Sizing 

General shop 

Complete cycle 

Roasting and retoring. . . . 

Concentrating 

Concrete operations 

Mechanic 

Load, haul, dump electric 

Rock sawing 

Bulldozing 

Pelletizing 

Hoisting 

Mining machine 

Load, haul, dump gas 

Technical services 

Load, haul, dump diesel. . 

Total 

Total metal and nonmetal. 



786 
103 
152 

57 
762 

89 

808 

2,101 

561 

2,215 

374 

232 

640 

148 

613 

136 

189 

150 

651 

400 

142 

442 

98 

43 

22 

194 

134 

4,116 



16,358 
16,747 



46.2 
55.3 
25.7 
24.6 
37.0 
27.0 
25.9 
31.3 

30.5 
30.1 
28.6 
18.5 
29.1 
16.9 
14.7 
22.8 
23.8 
27.3 
15.0 
15.2 
15.5 
16.5 
15.3 

7.0 
18.2 
20.1 
14.9 

9.9 

ND 

23.2 



0.90 
1.09 
.47 
.46 
.71 
.51 
.48 
.58 

.54 
.57 
.51 
.32 
.48 
.35 
.38 
.45 
.45 
.56 
.34 
.34 
.43 
.35 
.32 
.21 
.46 
.35 
.29 
.25 
ND 
.43 



1,232 

111 

198 

56 

622 

61 

1,047 

2,450 

753 

4,402 

552 

133 

936 

196 

479 

173 

268 

67 

559 

574 

117 

519 

204 

89 

39 

306 

112 

7,999 



24,254 
24,755 



39.8 
27.0 
27.3 
17.9 
22.7 
18.0 
22.2 
21.9 

21.0 

18.5 

19.4 

15.8 

18.1 

16.8 

11.5 

13.9 

15.7 

11.9 

10.7 

10.6 

6.8 

10.2 

13.7 

10.1 

5.1 

7.5 

5.4 

6.2 

ND 

15.1 



0.83 
.54 
.49 
.48 
.47 
.47 
.46 
.44 

.44 
.40 
.39 
.39 
.37 
.37 
.37 
.36 
.31 
.31 
.30 
.30 
.29 
.28 
.26 
.26 
.25 
.24 
.24 
.23 
ND 
.34 



GM C/TLV Geometric mean concentration-to-TLV ratio. 

N Sample size (number of samples). 

ND Not determined. 

TLV Threshold limit value. 

*Not included are machine mucking, hand mucking, timbering, rock 
ing, diamond drilling, load, haul, dump compressed air, track crew, 
operations, dredging, and jet piercing. 



bolting, 
slurry 



backfill- 
, chemical 



flour, which all have high quartz con- 
tents. The high quartz content results 
in a low TLV, meaning that dust controls 
must be very effective in order for the 
baggers to avoid overexposure. 

Of the 28 occupations listed in table 
6, 23 showed a reduction in the GM C/TLV 
from the first to the second time period, 
although 5 showed slight GM C/TLV 
increases (supply, blasting, admini- 
stration, general shop, and hoisting). 



Blasting and administration showed de- 
creases in the percentage of samples 
exceeding the TLV, and the other three 
either showed slight increases or re- 
mained the same. 

There were extreme differences in N 
among the occupational categories. The 
load, haul, dump diesel operator code was 
used 12,115 times, but the code for min- 
ing machine was used only 61 times. The 
explanation for the extreme difference 



14 



is that most metal 
diesel equipment, 
continuous miners, 
of rock breaking 
mines is drilling 
for a few industr 
trona, potash, and 
ing methods used 
adapted (1). 



and nonmetal mines use 

but relatively few use 

The principal method 

in underground noncoal 

and blasting, except 

ial minerals such as 

gypsum, for which min- 

in coal mines can be 



Commodity, Location, and Time 

It was possible to further subdivide 
the RQ data using location (i.e., under- 
ground mines, surface mines, or mills) as 



a variable, but in doing this the problem 
of inadequate sample size became very 
evident in specific subsets. However, 
despite this limitation, problem areas 
can be identified with such an analysis. 
Table 7 utilizes the GM C/TLV to show a 
commodity, location, and time analysis 
for the same commodity groups as are 
listed in table 5. Each of the mining 
locations includes commodities that pose 
potential problems . Underground molyb- 
denum and clay and shale mines have GM 
C/TLV s that were considerably higher 
than the mean for all underground loca- 
tions. The same was true for surface 



TABLE 7. - RQ GM C/TLV by commodity, location, and time period 



Commodity 1 


Underground 


Surface 


Mill 




1974-77 


1978-81 


1974-77 


1978-81 


1974-77 


1978-81 


Barite 


1.74 + 

0.51 + 

.68 * 

.83 * 

ND 

ND 
.55 * 

ND 
.45 * 

ND 

.07 + 
.46 * 

ND 
.78 + 

ND 
.67 + 

ND 

.30 + 
.47 * 

ND 

ND 

ND 

.50 * 
.27 + 


ND 
0.51 ++ 
.63 * 
.64 * 

ND 

ND 
.40 * 

ND 
.41 * 

ND 

ND 
.42 * 

ND 
.38 + 

ND 
.38 -H- 

ND 

.31 + 
.32 * 

ND 

ND 

.58 + 
.36 * 
.19 + 


ND 
0.55 + 

ND 

.35 * 
.35 + 
.27 ++ 
.58 + 
.45 * 
.31 * 
.22 ++ 
.42 + 

ND 
.24 * 

ND 

.30 ** 
.44 * 
.33 * 
.51 * 
.30 + 
.71 + 

ND 

.20 * 
.24 ** 
.39 + 


ND 
0.24 * 

ND 

.36 * 
.37 ++ 
.49 * 
.30 + 
.41 ** 
.36 * 
.22 -H- 
.37 + 

ND 
.32 * 

ND 

.30 ** 
.44 * 
.23 * 
.28 + 
.19 ++ 
.18 + 
.22 -H- 
.22 * 
.21 ** 
.20 ++ 


1.32 + 
0.96 * 
1.27 + 
.78 ** 
.64 ++ 
1.07 ++ 
.99 + 
.53 * 
.54 * 
.38 * 
1.29 + 
.31 * 
.35 * 
.21 + 
.59 * 
.83 * 
.38 * 
.40 ++ 
.26 -H- 
1.00 + 
.56 + 
.45 * 
.39 ** 
.18 + 


0.90 + 




1.09 * 




.94 + 




.68 ** 




.45 + 




.62 * 




.61 * 




.50 * 




.45 * 
.37 * 


Talc 


.41 ++ 




.37 ++ 




.53 * 




.34 + 
.59 * 




.80 * 




.35 * 




.31 + 
.26 * 




.33 ++ 


Mica 


.32 ++ 




.30 * 




.28 ** 




.23 + 




.52 


.40 


.31 


.28 


.55 


.46 



ND 

+ 



* 
** 



Not determined 
10-49 samples. 
50-99 samples. 
100-999 sample: 
More than 999 



because there were less than 10 samples 



samples. 
Listed in same order as in table 5 to allow comparison. 



15 



miscellaneous stone, sandstone, and gran- 
ite mines, although the magnitude of the 
GM C/TLV's was lower. Of the three loca- 
tion categories, mills had the highest GM 
C/TLV's. Within the mill category, mis- 
cellaneous nonmetal mills had the highest 
GM C/TLV's, and barite, molybdenum, and 
sandstone mills followed close behind. 
One problem with this analysis is that 
samples taken at some mills were grouped 
together with the surface and reported 
with surface mine location codes. Sam- 
ples collected indicated that exposures 
in mills were generally higher than those 
in surface mines. Therefore, the effect 
of grouping some mill samples with the 
surface mine samples would be to raise 
the surface mine exposures and decrease 
the difference between mill and surface 
mine exposures. Unfortunately, there was 
no way to separate these data. 

The data shown in table 7 are useful 
for identifying locations with higher RQ 
exposures. Listed below are the eight 
locations with the highest GM C/TLV's for 
1978-1981. At least 50 RQ samples were 
collected at each of these locations dur- 
ing this time period. The first value 
listed for each location is the GM C/TLV, 
and the percentage of samples greater 
than or equal to the TLV is shown in 
parentheses. 

Misc. nonmetal milling 1.09 (56.0) 

Sandstone milling 80 (41.7) 

Clay and shale milling 68 (33.7) 

Clay and shale underground 

mining 64 (27.3) 

Molybdenum underground mining .63 (31.5) 

Misc. stone milling 62 (29.1) 

Gold and silver milling 61 (30.0) 

Sand and gravel milling 59 (30.1) 

The bulk of the samples collected from 
miscellaneous nonmetal mills came from 
two tripoli mills with histories of dust 
problems. These and similar mills have 
received special attention from MSHA and 
NIOSH because they produce a finely 
ground product with a high quartz con- 
tent, and because acute cases of sil- 
icosis in young workers have been 
identified at these mills (14). An occu- 
pational breakdown of data collected from 
these mills showed that general laborers, 



baggers, and workers involved with crush- 
ing and other mill activities encountered 
exposures for which the GM C/TLV exceeded 
1.00. The GM RQ CONC for these facili- 
ties was 0.65 mg/m 3 , and the average 
quartz content of each sample was 60 pet, 
resulting in an average TLV of 0.17 mg/ 
m 3 . Dust control at these plants would 
have to be excellent in order for them to 
meet the RQ TLV. 

Workers in metal and nonmetal mills are 
generally exposed to more quartz dust 
than their counterparts in mining activ- 
ities. This is illustrated in figure 2, 



1,000 



100 — 



10 



1 1 1 1 1 1 1 I 


i 


- 


< n 




' w 


- 


'11 


- 


fit 




1 4 


/ 
/ 


/ 
/ 
/ 


// 


/ 1 / - 


: / A 


V 


<' /// 


~ 


'' /// 




'' // 




* / / 


_ 


* / / 




* / / 




* * / 




' -V / 




/ y / 




/ ." / 




/ ." / 


~ 


/ " / 




- //// 




- // 


- 


- 


.//// 


- 


KEY 








Other surface facilities 




• Underground mining 


- 


i i i i i i i i 


1 



10 20 30 40 50 60 70 
CUMULATIVE, pet 



80 90 100 



FIGURE 2. - Cumulative frequency plot of 
quartz concentrations grouped by location. 



16 



a cumulative frequency plot of quartz 
concentrations grouped by location for 
1978-81. Quartz concentrations are 
derived by multiplying the quartz content 
by concentration. Samples from mills 
contained more quartz than samples from 
other locations. A sample containing 100 
ug/m 3 or more of quartz always exceeds 
the RQ TLV when the respirable dust con- 
centration is less than 10 mg/m 3 , and 26 
pet (838) of the samples from mills 
exceeded this level. This compares to 8 
pet of the underground and surface mines 
samples and 14 pet of the other surface 
facilities samples. 

Commodity and Occupation Group 

As previously mentioned, the sample 
size N becomes smaller every time the RQ 
data are partitioned into smaller sub- 
groups. One way to deal with this prob- 
lem is to group similar codes together, 
thus making fewer subgroups. An example 
of this has already been shown in table 
4, in which all the commodities were 
grouped into four categories and all the 
location codes were grouped into three 
categories. However, table 4 does not 
show occupation, which is also a variable 
of interest. In order to group the codes 
in a meaningful manner, several param- 
eters must be considered. These include 
N for each code, the similarity of codes, 
and the distributions of dust concentra- 
tion and C/TLV. For some commodity and 
occupation codes, N is very large; for 
example, limestone and load, haul, dump 
diesel can stand alone as individual 
groups. 

Using the parameters listed above, the 
codes for location, commodity, and occu- 
pation were grouped for further analysis. 
The location codes were logically grouped 
by similar mine activities defined as (1) 
underground or surface mining and (2) 
surface mineral processing including 
milling. Since 24 commodities and 28 
occupations accounted for 97 pet of the 
1974-81 data, only these commodities and 
occupations were included in the consoli- 
dated commodity and occupation groups. 
This eliminated data from 20 commodities 



and 12 occupations, which are listed 
below tables 5 and 6. The remaining 
codes were grouped into 8 commodity 
groups and 9 occupation groups. Both 
sets of groups are listed below. The 
commodity groups included — 

1. Limestone. 

2. Sandstone. 

3. Stone; cement, granite, lime, 
slate, traprock, and miscellaneous stone. 

4. Metal; copper, gold and silver, 
iron, lead and zinc, molybdenum, and 
uranium. 

5. Clay and shale. 

6. Miscellaneous nonmetals. 

7. Nonmetals; barite, feldspar, fluor- 
spar, gypsum, mica, phosphate rock, and 
talc. 

8. Sand and gravel. 

The occupation groups included — 

1. Bagger. 

2. Crushing. 

3. Load, haul, dump; electric, diesel, 
gasoline, and bulldozer. 

4. Drilling; rotary and percussive. 

5. Grinding and sizing. 

6. Finishing; roasting and retorting; 
drying, filtering, and thickening; con- 
centrating; and pelletizing. 

7. General labor; including complete 
cycle and general shop work. 

8. Welder and mechanic. 

9. Other; slushing, blasting, rock 
sawing, mining machine operator, concrete 
operations, hoisting, supply handling, 
technical services, and administration. 



17 



The GM C/TLV s and percentages of samples 
greater than the TLV for these commodity 
and occupation groups are shown in tables 
A-6 and A-7. 

The data were further broken down into 
the two time periods used in the previous 
analyses, the periods before and after 
passage of the Mine Health and Safety Act 
of 1977 (1974-77 and 1978-81). Combining 
the 8 commodity groups, 9 occupation 
groups, 3 location groups, and 2 time 
periods creates a 8x9x3 x2 matrix 
with 432 cells for which the GM C/TLV, GM 



CONC, GSD, and other statistics can be 
calculated by a computer program in 
MIDAS. The table produced by this pro- 
gram is too extensive to be reproduced in 
this report, but the highlights are shown 
in table 8. The computer program that 
calculated the figures in table 8 is also 
capable of calculating the same values 
for different commodity, occupation, 
location, and time groups (and in fact 
this was done before the final grouping 
was adopted). There are 28 occupation, 
commodity, and location combinations 
listed in table 8. For each, N was at 



TABLE 8. - Occupation groups with highest RQ exposures 1 
(In order of decreasing GM C/TLV for 1978-81) 



Occupation group 



Commodity group 



Location 
group 



N 



> TLV, pet 



GM CONC, 
mg/m 3 



GM C/TLV 



General labor 

Bagger 

Do 

Do 

General labor 

Crushing 

Grinding, sizing. . . 

Bagger 

Grinding, sizing. . . 

Bagger 

Other 

Crushing 

Do 

Drilling 

Grinding, sizing. . . 

Finishing 

General labor 

Finishing 

Grinding, sizing. . . 

Drilling 

Bagger 

Load, haul, dump... 

Drilling 

Crushing 

Finishing 

Grinding, sizing. . . 

Crushing 

Finishing 



Misc. nonmetal... 

Sandstone 

Misc. nonmetal... 

Sandstone 

...do 



.do, 



• • •do •••••••••••• 

Clay and shale... 
Sand and gravel.. 
...do 



Clay and shale. . . 

...do 

Metal 

Sandstone 

Clay and shale... 
Sand and gravel.. 
Clay and shale... 
...do 



Sandstone 

Limestone 

Stone 

Clay and shale. . . 

Stone 

Metal 

Sandstone 

Stone 

...do 

Sand and gravel.. 



M 

S 

M 

M 

M 

M 

M 

M 

M 

M 

M 

M 

UG 

S 

M 

M 

M 

M 

S 

UG 

M 

UG 

S 

M 

M 

M 

M 

S 



64 

60 

100 

191 

88 

135 

85 

300 

98 

196 

81 

62 

53 

57 

143 

183 

114 

150 

93 

112 

93 

54 

620 

409 

137 

134 

350 

100 



75.0 
73.3 
62.0 
52.9 
53.4 
40.7 
45.9 
41.7 
42.9 
38.3 
39.5 
37.1 
34.0 
36.8 
32.9 
32.8 
29.8 
29.3 
35.5 
26.8 
23.7 
16.7 
29.8 
25.4 
24.8 
21.6 
21.4 
17.0 



0.69 
.40 
.82 
.44 
.52 
.42 
.37 

1.16 
.48 
.45 
.91 
.72 
.69 
.44 
.83 
.43 
.75 
.71 
.26 

1.43 
.68 
.79 
.56 
.45 
.45 
.66 
.60 
.32 



1.89 
1.58 
1.54 
1.25 
1.05 
.90 
.90 
.85 
.84 
.84 
.77 
.75 
.73 
.73 
.72 
.66 
.64 
.62 
.62 
.60 
.58 
.56 
.56 
.56 
.53 
.52 
.52 
.52 



GM CONC Geometric mean concentration. 

GM C/TLV Geometric mean concentration-to-TLV ratio. 

M Mill. 

N Sample size (number of samples). 

S Surface. 

TLV Threshold limit value. 

UG Underground. 



Based on the 1978-81 data with a minimum N of 50 and a minimum GM C/TLV of 0.50. 



18 



least 50 and the GM C/TLV was at least 
0.50; in all but two groups, more than 20 
pet of the samples exceeded the TLV. 

Occupations in sandstone, clay and 
shale, and miscellaneous nonmetal mines 
and mills account for 17 of the occupa- 
tion, commodity, and location groups 
listed in table 8. Eight of the ten 
highest ranked groups fell into one of 
these categories. The four sand and 
gravel combinations in table 8 (grinding, 
sizing — mill, bagger — mill, finishing — 
mill, and finishing — surface) primarily 
represent data from industrial sand pro- 
cessing plants. A mine-by-mine investi- 
gation of the ten highest ranked groups 
revealed that the results for these occu- 
pational groups were greatly influenced 
by samples collected at industrial sand 
facilities. The industrial sand pro- 
ducers, including the producers of silica 
flour, are classified by MSHA in the 
sandstone, sand and gravel, and mis- 
cellaneous nonmetal categories. The 
extremely high quartz content, small par- 
ticle size, and abrasive nature of sand 
make sand dust difficult to control and 
result in high GM C/TLV s at these estab- 
lishments. An anomaly in the table is 
that certain finishing occupations occur 
twice, once with a mill location and once 
with a surface mining location. One rea- 
son for this is that some mines do not 
have a separate identifiable mill, and 
thus it was necessary to use the surface 
mine location code; however, in some 
cases this anomaly resulted from failure 
to use the correct code. 

The occupational groups most frequently 
listed in table 8 are bagger, crushing, 
grinding, and sizing. They account for 
16 of the 28 high exposure groups. The 
data shown in table 8 indicate that work- 
ers in these areas are frequently exposed 
to levels exceeding the TLV. The occupa- 
tion bagger occurs 6 times in table 8, 
accounting for 940 out of a total of 
1,232 (or 76 pet of the total) samples, 
for the period 1978-81. Baggers had the 
highest risk of overexposure of any 
occupation; 40 pet of all bagger samples 
exceeded the TLV. 



Three underground mine groups appear 
in table 8: Drilling — limestone, load- 
haul-dump — clay and shale, and crushing — 
metal. Drilling at surface sandstone and 
other stone quarries also had an above- 
average risk of excessive exposure. 

Another way of estimating the risk from 
exposure is to calculate the quartz con- 
tent in each sample and plot the fre- 
quency distributions, as was done in fig- 
ure 2 for location groups. Figure 3 
shows the 1978-81 frequency distributions 
for seven of the eight commodity groups 
shown in table 8. The other group, 
stone, is not included because the 
plotted line for stone closely resembles 
the line for sand and gravel. Quartz 





1 w 

90 


1 III 
7 Ft 


1 1 1 1 ^^J^^ 


r- 






/ / E/ ' 






80 




r c/ / / 
/ / A / 






70 






- 


^_ 










o 










Q. 










uj" 


60 






_ 


> 










1- 










< 










_l 










Z> 
2 


50 






- 


Z> 










o 






KEY 






40 


1 // / // A 

1/ / / B 

/// / / c 


Misc nonmetal 
Sandstone 
Clay and shale 






30 


r// / D 
// / E 

V / F 

// G 


Sand and gravel 
Metal 
Nonmetal 
Limestone 






20 


l l 1 1 I 


Mil 1 


1 



10 



50 



100 



400 



QUARTZ, /xg/m 3 

FIGURE 3. - Cumulative frequency plot of 
quartz concentrations grouped by commodity. 



content is important in determining TLV's 
and in assessing the impact of changes in 
regulations. NIOSH has proposed an occu- 
pational standard of 50 pg/m 3 of crys- 
talline silica as a time-^weighted average 
to protect workers for up to a 10-h work 
day and 40-h work week over a working 
lifetime (13). This contrasts with the 
100-pg/m 3 standard which is used by the 
Occupational Safety and Health Admini- 
stration (OSHA) and is now being proposed 
by MSHA. Figure 3 clearly shows the 
effect these two levels would have had on 
the rate of past overexposure. If the 
NIOSH proposed standard had been in 
effect, many more samples would have been 
considered to represent overexposures. 
Miscellaneous nonmetals, sandstone, and 
clay and shale are commodities with high 
quartz contents , and therefore they would 
have had a greater percentage of samples 
exceeding a lower standard than would 
limestone producers. 



19 



High concentrations of respirable dust 
with less than 1 pet quartz content could 
constitute overexposures if the standard 
were changed to 100 pg/m 3 of quartz. 
Table 9 lists 15 occupation groups that 
are not listed in table 8 but have GM 
dust concentrations greater than 0.50 
mg/m 3 . Again, mill locations predom- 
inate, but table 9 also includes four 
underground occupational groups for metal 
and limestone mines. Other commodities 
with high dust levels were identified 
from total nuisance dust data not shown; 
they include underground salt, trona, and 
potash mines where quartz is not a con- 
stituent of the host rock. 

Many of the workers sampled by MSHA 
inspectors were wearing respirators at 
the time the sample was collected, so the 
actual exposure of the individual was 
somewhat lower than the reported expo- 
sure. In 1981, 4,345 RQ samples were 



TABLE 9. - Other occupation groups with high RQ exposures 1 
(In order of decreasing GM CONC) 



Occupation group 



Commodity group 



Location 


N 


> TLV, 


GM 


GM CONC, 


group 




pet 


C/TLV 


mg/m 3 


M 


98 


15.3 


0.43 


0.77 


M 


342 


21.9 


.43 


.68 


M 


182 


11.5 


.32 


.68 


M 


120 


20.0 


.41 


.67 


UG 


231 


8.2 


.31 


.67 


M 


70 


7.1 


.35 


.58 


M 


532 


10.3 


.30 


.58 


UG 


173 


9.2 


.37 


.56 


M 


162 


11.1 


.32 


.56 


UG 


204 


18.6 


.46 


.55 


UG 


466 


11.4 


.40 


.54 


M 


50 


14.0 


.33 


.50 


M 


122 


22.1 


.45 


.52 


S 


510 


22.6 


.47 


.50 


S 


920 


9.0 


.26 


.50 



Bagger . 

General labor..., 
Grinding, sizing. 

Finishing 

Load, haul, dump. 
Welder, mechanic. 

Crushing 

Drilling 

General labor..., 

Other , 

Load , haul , dump . 

Finishing , 

Load, haul, dump, 

Crushing , 

Do , 



Nonmetal. < 
Stone...., 
Limestone, 
Stone. . . . , 
Limestone, 
Stone...., 
Limestone, 
Metal...., 
Limestone, 
Metal...., 
...do...., 



Nonmetal 

Clay and shale, 

Stone 

Limestone 



GM CONC 
GM C/TLV 
M 
N 
S 

TLV 
UG 

^ased 
mg/m 3 . 



Geometric mean concentration. 
Geometric mean concentration-to-TLV ratio. 
Mill. 

Sample size (number of samples). 
Surface. 

Threshold limit value. 
Underground, 
on the 1978-81 data with a minimum N of 50 



and a minimum GM CONC of 0.50 



20 



collected, and 17.35 pet of these samples 
equaled or exceeded the TLV (table 3). 
In instances where the C/TLV was 1.0 or 
more, 68 pet of the workers were wearing 
respirators. The percentage wearing res- 
pirators decreased as exposure declined 
below the TLV, but 52 pet of the 261 
workers exposed where C/TLV ranged be- 
tween 0.80 and 0.99 were wearing respir- 
ators. Data from table 8 showed that 
general laborers and baggers employed in 
sandstone and miscellaneous nonmetal 
mills had the greatest risk of RQ 



overexposure. MSHA records show that of 
the 89 samples collected on baggers at 
these properties in 1981, 64 pet had 
C/TLV ratios of 1.0 or greater, and of 
these, 52 workers (91 pet) wore respir- 
ators. Of general laborers with similar 
exposures, 82 pet wore respirators. MSHA 
regulations require the use of respira- 
tory protection on an interim basis in 
situations where overexposures occur and 
the implementation of engineering con- 
trols to permanently protect workers. 



SUMMARY AND CONCLUSIONS 



The objective of this report was to 
statistically analyze records of RQ expo- 
sures collected by MSHA from 1974 through 
1981 to identify commodities and occupa- 
tions associated with high quartz dust 
exposure. The analysis was conducted 
using the Mine Inspection Data Analysis 
System (MIDAS), a computerized system 
designed by the Bureau with the collabor- 
ation of MSHA. 

Editing the RQ data left 41,502 records 
for analysis, and these were unequally 
distributed among the occupation, commod- 
ity, and location codes used by MSHA. 
The primary reason for the unequal dis- 
tribution is that MSHA is mandated by law 
to inspect all properties within its 
jurisdiction, and this type of sampling 
strategy would tend to assess dust levels 
in all types of mines rather than in 
high-risk areas only. 

The C/TLV was shown to approximate a 
log-normal distribution, and the GM of 
this distribution was shown to be a good 
index of exposure because it relates dust 
concentration to the TLV, which is deter- 
mined by a formula dependent upon the 
percentage of quartz found in the sample. 
The greater the quartz content the lower 
the TLV. Use of the C/TLV as an index of 
exposure places an equal weight upon both 
the quartz content and the dust concen- 
tration of the sample; hence the ranking 
of high-risk subpopulations reflects 
both. Only samples that contained more 
than 1.0 pet quartz were considered in 
this analysis; samples with less than 1.0 



pet quartz were reclassified as respir- 
able nuisance dust. 

The GM CONC for all samples was calcu- 
lated to be 0.43 mg/m 3 with a GSD of 
2.98. The median concentration was 0.40 
mg/m 3 , and 18.37 pet of all samples 
(7,623 samples) exceeded the TLV. The GM 
C/TLV was 0.37 with a GSD of 3.40. The 
TLV calculated from these data was 1.16 
mg/m 3 with an average quartz content of 
6.6 pet. The range of quartz content was 
between 1.0 and 98.0 pet. 

The highest GM C/TLV s and the highest 
percentage of overexposure were in mills, 
for workers involved in bagging, crush- 
ing, grinding, sizing, and general labor 
activities. Exposures were highest in 
industrial sand processing plants. The 
respirable dust concentrations measured 
at these plants were not the highest, but 
the allowable level of exposure was the 
lowest because the samples contained high 
percentages of quartz. These mills are 
located at mines classified as sandstone, 
sand and gravel, miscellaneous nonmetal, 
and miscellaneous stone producers. Other 
mills with high exposures were molyb- 
denum, clay and shale, barite, and gold 
and silver producers. Crushing, grind- 
ing, sizing, and general labor activities 
at limestone mills were also dusty activ- 
ities, but the quartz content of the rock 
was low, making these exposures less 
serious. More than 100,000 workers were 
employed in noncoal mills in 1981, which 
was more than 40 pet of the entire metal 
and nonmetal mine workforce, so the 



21 



problem was significant in terms of the 
number of potentially exposed workers. 

Fewer samples were collected in under- 
ground mines, and exposures were gener- 
ally lower than in mills ; but several 
commodities mined underground had rela- 
tively high levels of exposure. Under- 
ground molybdenum, clay and shale, iron, 
and miscellaneous nonmetal mines stood 
out as having higher GM C/TLV's and a 
greater percentage of overexposures. 
Underground mine activities with higher 
than average exposures were crushing, 
drilling, and diesel vehicle haulage. 
Slushing was also a dusty underground 
mine occupation, but very few samples 
were collected. 

Surface mines had the lowest overall 
exposures, and in these mines drillers in 
stone quarries had the highest exposures. 



Crushing at limestone mines was also a 
dusty occupation, but their relativly low 
quartz contents resulted in higher allow- 
able dust levels. 

The analysis showed that quartz concen- 
trations varied when data were grouped by 
location and commodity. About 3,200 more 
samples would have exceeded the exposure 
limit had the NIOSH recommended PEL of 50 
ug/m 3 been in effect. Quartz content was 
highest in samples from mills producing 
products such as industrial sand and 
tripoli, and in underground mines pro- 
ducing molybdenum and clay and shale. 
Specific dust control research projects 
should focus on these high-risk areas 
with the intention of developing controls 
that can be transferred to other seg- 
ments of the industry with minimum 
modification. 



REFERENCES 



1. National Materials Advisory Board. 
Measurement and Control of Respirable 
Dust in Mines. Natl. Acad. Sci., Wash- 
ington, DC, NMAB-363, 1980, 405 pp. 

2. National Institute for Occupational 
Safety and Health. Occupational Health 
Guidelines for Crystalline Silica. DHHS 
(NIOSH) 81-123, Jan. 1981, 121 pp. 

3. Watts, W. F., R. L. Johnson, D. J. 
Donaven, and D. R. Parker. An Introduc- 
tion to the Mine Inspection Data Analysis 
System (MIDAS). BuMines IC 8859, 1981, 
41 pp. 

4. Jahsman, W. E., R. L. Johnson, 
D. J. Donaven, and W. F. Watts. MIDAS 
User's Manual. Internal BuMines manual, 
1981, 210 pp.; available from the Divi- 
sion of Automatic Data Processing, Bu- 
Mines, Denver, CO. 

5. U.S. Congress. The Federal Mine 
Safety and Health Act of 1977. Public 
Law 91-173, as amended by Public Law 95- 
164, Nov. 9, 1977, 83 Stat. 



6. U.S. Code of Federal Regulations. 
Title 30 — Mineral Resources; Chapter 1 — 
Mine Safety and Health Administration; 
July 1, 1981. 

7. American Conference of Govern- 
mental Industrial Hygienists. TLV's — 
Threshold Limit Values for Chemical Sub- 
stances in Workroom Air Adopted by the 
ACGIH in 1973. Cincinnati, OH, 1973, 
54 pp. 

8. . Documentation of the 

Threshold Limit Values. 4th ed. , 1980, 
Cincinnati, OH, pp. 364-465. 

9. Mine Safety and Health Administra- 
tion. Fiscal Year 1982 Health Mine Rank- 
ing and Inspection Guideline. Available 
from Health Div. for Metal and Nonmetal 
Safety and Health, Mine Safety and Health 
Administration, Arlington, VA, 1982, 
3 pp. 

10. Dixon, W. J. (ed.). BMDP Sta- 
tistical Software. Univ. of CA Press, 
1981 ed., 1981, 715 pp. 



22 



11. Freund, J. E., and R. E. Walpole. 
Mathematical Statistics. Prentice-Hall, 
1980, pp. 206-211. 

12. JRB Associates. Technological 
Control of Asbestos and Silica at Mines 
and Mills, Task 1 Progress Report Expo- 
sure Profiles. (NIOSH contract 210- 
81-4104), Oct. 30, 1981; for inf., 
contact Roy Flemming, TPO, NIOSH, Wash- 
ington, DC. 



13. National Institute for Occupa- 
tional Safety and Health. Criteria for a 
Recommended Standard. . .Occupational Expo- 
sure to Crystalline Silica. HEW (now 
HHS), 1974, HEW 75-120. 



14. Center for 
Silicosis-Illinois. 
tality Wkly. Rep. , 
pp. 205-206. 



Disease Control. 

Morbidity and Mor- 

v. 29, May 9, 1980, 



23 



APPENDIX A.--MSHA SAMPLING CODES AND DESCRIPTORS AND SELECTED GM C/TLV DATA 

TABLE A-l. - MSHA contaminant codes for personal samples and number of edited records 
for each contaminant 



Code 



Type of measurement 

RQ 

Respirable nuisance dust 

Talc, nonasbestiform 

Nuisance dust 1 

Cristobalite, respirable 

Tridymite, respirable 

Mercury vapor 

Lead 1 

Cadmium 1 > 

Arsenic and compounds 1 

Manganese 1 

Beryllium 1 

Iron oxide 1 

Asbestos , fibers >5ym. ....... 

Cobalt 1 

Copper fume 1 

Molybdenum 1 

Nickel 1 

Vanadium fume ^ 

Zinc oxide fume 1 

Chromium 1 

Oil mist 1 

Airborne silica dust, total.. 

Welding fume 1 

Noise, dosimeter measurement. , 

Sound level meter measurement, 

Magnesium oxides 1 

Aluminum oxides 1 

Titanium oxides 1 

pirable quartz dust, 
particulate. 



Unit of measure used 

mg/nr* , 

mg/m 3 

mppcf , 

mg/m 3 , 

mg/m 3 

mg/m 3 

mg/m 3 

mg/m 3 

mg/m 3 

yg/m 3 

yg/m 3 

ug/m 3 

mg/m 3 

fibers/cm 3 

mg/m 3 

mg/m 3 

mg/m 3 

mg/m 3 

Pg/m 3 

mg/m 3 

mg/m 3 

mg/m 3 

mg/m 3 

mg/m 3 

pet 

dBA 

mg/m 3 

mg/m 3 

mg/m 3 



Number of records 



1... 
2... 
3... 
4... 
11.. 
12.. 
13.. 
14.. 
15.. 
16.. 
17.. 
18.. 
19.. 
20.. 
21.. 
22.. 
23.. 
24.. 
27.. 
28.. 
29.. 
31.. 
34.. 
35.. 
40.. 
41.. 
81.. 
82.. 
83.. 



41,566 

11,582 

283 

13,415 

168 

3 

70 

1,357 

379 

457 

806 

332 

1,430 

1,365 

527 

714 

490 

609 

508 

860 

830 

56 

12,515 

1,030 

86,229 

3,601 

898 

945 

717 



RQ Res 
1 Total 



24 



TABLE A-2. - MSHA contaminant codes for area samples and number of records for each 
contaminant 



Code 



Type of measurement 

Mercury vapor 

Radon daughter measurement 

Nitrogen oxide 

Nitrogen dioxide 

Nitrogen oxides 

Carbon monoxide 

Carbon dioxide 

Aldehydes 

Ammonia 

Hydrogen sulfide 

Sulfur dioxide 

Chlorine 

Sulfuric acid mist 

Hydrogen cyanide 

Carbon disulfide 

Perchlorethylene. 

Phosgene. 

Oxygen 

Hydrocarbons, total 

Methane 



Unit of measure used 

mg/nr* 

WL 

PPm 

PPm 

PPm 

PPm 

PPm 

Ppm 

PPm 

PPm 

PPm 

PPm 

mg/m 3 

PPm 

PPm 

PPm 

PPm 

pet 

PPm 

pet 



Number of records 



13.. 
50.. 
70.. 
71.. 
72.. 
73.. 
74.. 
75.. 
76.. 
77.. 
78.. 
79.. 
80.. 
87.. 
88.. 
89.. 
90.. 
91.. 
92.. 
93.. 



810 

27,311 

180 

9,261 

431 

28,170 

24,292 

132 

259 

2,450 

577 

22 

1 

248 

4 

17 

20 

15,663 

109 

28,217 



TABLE A-3. - RQ samples collected 1974-81 for each MSHA occupation code 



Code 



Occupation 



Number of 

personal 

samples 



Code 



Occupation 



Number of 

personal 

samples 



1... 
2... 

-J • • • 

4... 
5... 

6. . • 
7... 
8. . . 
9... 
10.. 
11.. 
12.. 

13.. 

14.. 

15.. 

16.. 
17.. 
18.. 
19.. 



Slushing 

Machine mucking 

Hand mucking 

Timbering 

Rock bolting 

Backfilling 

Blasting. 

Rock sawing 

Drilling, percussive... 

Drilling, rotary 

Drilling, diamond 

Loading, hauling, dump- 
ing electric. 

Loading, hauling, dump- 
ing diesel. 

Loading, hauling, dump- 
ing gasoline. 

Loading, hauling, dump- 
ing compressed air. 

Mining machine operator 

Track crew 

Complete mining cycle. . 

Concrete operations.... 



214 
79 
58 
83 
78 
4 

113 

259 
1,855 

926 
66 

974 

12,115 

500 

158 

61 

36 

1,092 

217 



20... 
21... 

23... 

24... 
25... 
26... 
27... 
28. • . 
29... 
30... 

31... 
32... 

34... 
35... 
36... 
37... 
38. . • 
39... 
40... 



Hoisting 

Bulldozing 

Slurry 

General labor and 

cleanup. 

General shopwork 

Welding 

Mechanic 

Crushing 

Grinding 

Roasting, retorting.... 
Drying, filtering, and 

thickening. 

Sizing 

Concentrating 

Chemical operations.... 

Supply handling 

Technical services..... 

Administration 

Bagger 

Pelletizing 

Dredging 

Jet piercing 



132 

967 

79 

4,551 

344 
150 
1,210 
6,617 
1,384 
309 
1,314 

1,576 

457 

92 

350 

246 

365 

2,018 

302 

58 

99 



25 



TABLE A-4. - RQ samples collected 1974-81 for each commodity 



Commodity 



N 



Commodity 



N 



STONE 



Cement. . . , 
Granite. . , 

Lime , 

Limestone, 
Marble. . . , 



2,347 

3,509 

393 

9,752 

37 



Sandstone, 
Slate...., 
Traprock. , 
Misc. . ... , 



3,130 
266 
711 
468 



METAL MINES AND MILLS 



Antimony 

Bauxite (including alumina mills). 

Beryl 

Chromite 

Copper 

Gold and silver, lode and placer. . 
Iron 



33 

12 

2 



1,999 

874 

2,177 



Lead and zinc, 
Manganese. . . . , 
Molybdenum. . . . 

Titanium , 

Tungsten , 

Uranium , 

Mercury , 

Other metals. . 



905 

13 

526 

20 

38 

1,097 

6 

15 



NONMETAL MINES AND MILLS 



Asbestos , 

Barite , 

Boron minerals. 
Clay and shale, 

Feldspar , 

Fluorspar , 

Gypsum. ........ 

Magnesite , 

Mica 

Phosphate rock, 



3 
140 

7 
3,213 
193 
122 
180 
12 
134 
462 



Potash , 

Pumice , 

Salt , 

Sodium compounds , 

Sulfur , 

Talc, soapstone, and pyrophyllite, 

Gilsonite , 

Oil and/or shale. , 

Misc. nonmetals 1 , 



4 

31 

7 

11 



142 

2 

4 

741 



MISC. NONFUEL MINES AND MILLS 



Unspecified 1,031 



Sand and gravel | 6,725 

N Sample size (number of samples). 

1 0ther metals and miscellaneous nonmetals are defined in the Standard Industrial 
Classification (SIC) codes published in 1979 by the National Bureau of Standards. 



TABLE A-5. - RQ samples from metal and nonmetal mines, by location 



Category 



N 



Underground mine locations: 

Mill 77 

Shop 22 

Metal mine 3, 245 

Nonmetal mine 1,134 

Stone mine 455 

Open pit mine locations: 

Crushed stone mine 11,339 

Sand and gravel quarry 5,492 

Nonmetal mine 3, 050 

Metal mine 1,445 

N Sample size (number of samples). 



Category 



N 



Surface mill locations: 

Mill (bagging, screening, etc.) 5,575 

Crushing 4,210 

Grinding 1, 553 

Flotation and reagents 516 

Shop 313 

Misc 3, 068 



26 



TABLE A-6. - GM C/TLV's for 9 occupation groups used in table 8 
(In order of decreasing GM C/TLV for 1978-81) 



Occupation group 



1974-77 



N 



> TLV, pet 



GM C/TLV 



1978-81 



N 



> TLV, pet 



GM C/TLV 



Bagger , 

Drilling , 

General labor. . . < 

Crushing 

Grinding, sizing. 

Finishing 

Other 

Welder, mechanic. 
Load, haul, dump. 



786 
1,182 
2,862 
2,215 
1,402 

984 
1,035 

740 
5,152 



46.2 
26.7 
27.0 
30.1 
33.4 
26.6 
23.5 
16.5 
11.3 



0.91 
.49 
.52 
.57 
.60 
.48 
.43 
.36 
.27 



1,232 
1,559 
3,125 
4,402 
1,558 
1,398 
922 
620 
9,398 



39.7 
21.3 
20.0 
18.5 
19.8 
17.9 
16.0 
11.4 
6.7 



0.83 
.44 
.43 
.41 
.41 
.37 
.37 
.32 
.23 



GM C/TLV Geometric mean concent rat ion-to-TLV ratio. 
N Sample size (number of samples). 
TLV Threshold limit value. 

TABLE A-7. - GM C/TLV's for 8 commodity groups used in table 8 

(In order of decreasing GM C/TLV for 1978-81) 



Commodity group 



1974-77 



N 



> TLV, pet 



GM C/TLV 



1978-81 



N 



> TLV, pet 



GM C/TLV 



Misc. nonmetal. . 
Clay and shale. . 

Sandstone 

Stone. 

Sand and gravel. 

Metal , 

Nonmetal • . 

Limestone , 



242 
1,723 
1,363 
3,298 
2,082 
3,524 

672 
3,308 



43.4 
34.9 
37.4 
20.5 
21.8 
22.2 
27.7 
14.4 



0.83 
.65 
.67 
.40 
.37 
.46 
.56 
.29 



499 
1,490 
1,767 
4,396 
4,643 
4,054 

709 
6,444 



40.9 
28.3 
29.9 
16.8 
15.1 
13.2 
10.2 
6.6 



0.69 
.58 
.56 
.39 
.34 
.34 
.31 
.23 



GM C/TLV Geometric mean concent rat ion-to-TLV ratio. 
N Sample size (number of samples). 
TLV Threshold limit value. 



27 



APPENDIX B.--MSHA MINE HEALTH RANKING CRITERIA 



Rank A 

a. All underground uranium mines. 

b. All nonuranium underground mines 
where employees are exposed to radon 
daughters in excess of 0.10 WL. 

c. All mines and mills where employees 
are exposed to mineral fibers classified 
as asbestos or talc. 

d. All mines and mills where employees 
are overexposed to toxic or asphyxiant 
gases. 

e. All mines and mills where employees 
are overexposed to contaminants which 
have ceiling values recommended by the 
ACGIH. 

f . All mines and mills where employees 
are overexposed to particulates (except 
silica) and whose TLV's are less than 10 
mg/m 3 or 30 mppcf . 



their TLV: antimony, arsenic, beryllium, 
cadmium, chromium, cobalt, manganese, 
mercury, nickel, lead, uranium, and 
vanadium. 

j . Any other mine or mill where the 
inspector and supervisor believe that a 
serious health hazard exists. 

Rank B 

a. All mines and mills where employees 
are overexposed to particulates whose 
TLV's are 10 mg/m 3 or 30 mppcf. 

b. All mines and mills where employees 
are occasionally overexposed to dust con- 
taining 1 pet or more free silica. 

c. All mines and mills where employees 
are overexposed to fumes containing the 
following substances at or above half 
their TLV: antimony, arsenic, beryllium, 
cadmium, chromium, cobalt, manganese, 
mercury, nickel, lead, or vanadium. 



g. All producers of silica sand, glass 
sand, industrial sand, and silica flour; 
and all mines and mills where cristo- 
balite or tridymite occur. 

h. All mines and mills where employees 
are repeatedly overexposed to dust con- 
taining 1 pet or more of free silica. 



d. All mines and mills where employees 
are overexposed to noise. 

e. Any other mine or mill where the 
inspector and supervisor believe that a 
health hazard exists. 

Rank C 



i. All mines and mills where employees 
are exposed to dust containing the fol- 
lowing substances at or above one-half 



a. All mines and mills where 
cernible health hazard exists. 



no dis- 



28 



APPENDIX C. —ABBREVIATIONS USED IN THIS REPORT 

NOTE. — This listing does not include unit of measure abbreviations, which are listed 
after the table of contents, or abbreviations that are used only in the tables and 
identified below the tables. 

ACGIH American Conference of Governmental Industrial Hygienists 

C/TLV concentration-to-threshold limit value ratio 

GM geometric mean 

GM CONC geometric mean concentration 

GSD geometric standard deviation 

MAS Minerals Availability System 

MIDAS Mine Inspection Data Analysis System 

MSHA Mine Safety and Health Administration 

N sample size 

NIOSH National Institute for Occupational Safety and Health 

PEL permissable exposure limit 

RQ respirable quartz dust 

TLV threshold limit value 



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